Chapter

Pattern Recognition and Machine Intelligence

Volume 3776 of the series Lecture Notes in Computer Science pp 376-381

Systematically Evolving Configuration Parameters for Computational Intelligence Methods

  • Jason M. ProctorAffiliated withCollege of Information Science & Technology, Drexel University
  • , Rosina WeberAffiliated withCollege of Information Science & Technology, Drexel University

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Abstract

The configuration of a computational intelligence (CI) method is responsible for its intelligence (e.g. tolerance, flexibility) as well as its accuracy. In this paper, we investigate how to automatically improve the performance of a CI method by finding alternate configuration parameter values that produce more accurate results. We explore this by using a genetic algorithm (GA) to find suitable configurations for the CI methods in an integrated CI system, given several different input data sets. This paper describes the implementation and validation of our approach in the domain of software testing, but ultimately we believe it can be applied in many situations where a CI method must produce accurate results for a wide variety of problems.